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from __future__ import print_function |
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from collections import defaultdict, deque |
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import datetime |
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import math |
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import time |
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import torch |
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import torch.distributed as dist |
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import torch.backends.cudnn as cudnn |
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import errno |
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import os |
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import sys |
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class SmoothedValue(object): |
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"""Track a series of values and provide access to smoothed values over a |
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window or the global series average. |
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""" |
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def __init__(self, window_size=20, fmt=None): |
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if fmt is None: |
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fmt = "{median:.4f} ({global_avg:.4f})" |
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self.deque = deque(maxlen=window_size) |
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self.total = 0.0 |
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self.count = 0 |
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self.fmt = fmt |
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def update(self, value, n=1): |
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self.deque.append(value) |
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self.count += n |
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self.total += value * n |
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def synchronize_between_processes(self): |
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""" |
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Warning: does not synchronize the deque! |
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""" |
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if not is_dist_avail_and_initialized(): |
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return |
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t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') |
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dist.barrier() |
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dist.all_reduce(t) |
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t = t.tolist() |
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self.count = int(t[0]) |
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self.total = t[1] |
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@property |
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def median(self): |
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d = torch.tensor(list(self.deque)) |
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return d.median().item() |
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@property |
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def avg(self): |
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d = torch.tensor(list(self.deque), dtype=torch.float32) |
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return d.mean().item() |
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@property |
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def global_avg(self): |
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return self.total / self.count |
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@property |
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def max(self): |
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return max(self.deque) |
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@property |
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def value(self): |
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return self.deque[-1] |
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def __str__(self): |
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return self.fmt.format( |
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median=self.median, |
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avg=self.avg, |
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global_avg=self.global_avg, |
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max=self.max, |
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value=self.value) |
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class MetricLogger(object): |
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def __init__(self, delimiter="\t"): |
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self.meters = defaultdict(SmoothedValue) |
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self.delimiter = delimiter |
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def update(self, **kwargs): |
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for k, v in kwargs.items(): |
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if isinstance(v, torch.Tensor): |
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v = v.item() |
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assert isinstance(v, (float, int)) |
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self.meters[k].update(v) |
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def __getattr__(self, attr): |
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if attr in self.meters: |
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return self.meters[attr] |
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if attr in self.__dict__: |
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return self.__dict__[attr] |
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raise AttributeError("'{}' object has no attribute '{}'".format( |
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type(self).__name__, attr)) |
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def __str__(self): |
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loss_str = [] |
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for name, meter in self.meters.items(): |
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loss_str.append( |
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"{}: {}".format(name, str(meter)) |
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) |
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return self.delimiter.join(loss_str) |
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def synchronize_between_processes(self): |
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for meter in self.meters.values(): |
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meter.synchronize_between_processes() |
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def add_meter(self, name, meter): |
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self.meters[name] = meter |
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def log_every(self, iterable, print_freq, header=None): |
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i = 0 |
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if not header: |
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header = '' |
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start_time = time.time() |
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end = time.time() |
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iter_time = SmoothedValue(fmt='{avg:.4f}') |
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data_time = SmoothedValue(fmt='{avg:.4f}') |
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space_fmt = ':' + str(len(str(len(iterable)))) + 'd' |
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log_msg = self.delimiter.join([ |
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header, |
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'[{0' + space_fmt + '}/{1}]', |
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'eta: {eta}', |
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'{meters}', |
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'time: {time}', |
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'data: {data}', |
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'max mem: {memory:.0f}' |
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]) |
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MB = 1024.0 * 1024.0 |
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for obj in iterable: |
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data_time.update(time.time() - end) |
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yield obj |
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iter_time.update(time.time() - end) |
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if i % print_freq == 0: |
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eta_seconds = iter_time.global_avg * (len(iterable) - i) |
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
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print(log_msg.format( |
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i, len(iterable), eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), data=str(data_time), |
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memory=torch.cuda.max_memory_allocated() / MB)) |
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sys.stdout.flush() |
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i += 1 |
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end = time.time() |
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total_time = time.time() - start_time |
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total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
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print('{} Total time: {}'.format(header, total_time_str)) |
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def mkdir(path): |
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try: |
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os.makedirs(path) |
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except OSError as e: |
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if e.errno != errno.EEXIST: |
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raise |
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def setup_for_distributed(is_master): |
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""" |
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This function disables printing when not in master process |
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""" |
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import builtins as __builtin__ |
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builtin_print = __builtin__.print |
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def print(*args, **kwargs): |
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force = kwargs.pop('force', False) |
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if is_master or force: |
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builtin_print(*args, **kwargs) |
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__builtin__.print = print |
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def is_dist_avail_and_initialized(): |
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if not dist.is_available(): |
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return False |
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if not dist.is_initialized(): |
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return False |
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return True |
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def get_world_size(): |
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if not is_dist_avail_and_initialized(): |
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return 1 |
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return dist.get_world_size() |
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def get_rank(): |
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if not is_dist_avail_and_initialized(): |
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return 0 |
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return dist.get_rank() |
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def is_main_process(): |
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return get_rank() == 0 |
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def save_on_master(*args, **kwargs): |
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if is_main_process(): |
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torch.save(*args, **kwargs) |
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def init_distributed_mode(args): |
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if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: |
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rank = int(os.environ["RANK"]) |
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world_size = int(os.environ['WORLD_SIZE']) |
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print(f"RANK and WORLD_SIZE in environment: {rank}/{world_size}") |
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else: |
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rank = -1 |
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world_size = -1 |
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torch.cuda.set_device(args.local_rank) |
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torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank) |
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torch.distributed.barrier() |
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setup_for_distributed(is_main_process()) |
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if args.output_dir: |
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mkdir(args.output_dir) |
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if args.model_id: |
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mkdir(os.path.join('./models/', args.model_id)) |
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